Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation
A new arXiv paper argues that long-horizon robot manipulation needs memory, but that this memory does not have to live inside the action policy itself. The work proposes placing memory on the agent side and using it as guidance to steer action models, rather than relying solely on vision-language-action policies paired with planners and geometric tools. The approach targets tasks that span many steps, where extra depth or calibrated geometry is sometimes added to the control stack.